Overview

Dataset statistics

Number of variables20
Number of observations13645
Missing cells3336
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 MiB
Average record size in memory783.3 B

Variable types

Categorical12
Numeric8

Warnings

YearsOfExperince is highly correlated with GraduationYearHigh correlation
GraduationYear is highly correlated with YearsOfExperinceHigh correlation
CurrentCTC is highly correlated with ExpectedCTCHigh correlation
ExpectedCTC is highly correlated with CurrentCTCHigh correlation
BiasInfluentialFactor has 3336 (24.4%) missing values Missing

Reproduction

Analysis started2021-06-06 14:38:43.203271
Analysis finished2021-06-06 14:39:17.485822
Duration34.28 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size948.8 KiB
English
7510 
Hindi
4733 
Native
1402 

Length

Max length7
Median length7
Mean length6.203517772
Min length5

Characters and Unicode

Total characters84647
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish
ValueCountFrequency (%)
English7510
55.0%
Hindi4733
34.7%
Native1402
 
10.3%
2021-06-06T20:09:18.075763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:18.315036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
english7510
55.0%
hindi4733
34.7%
native1402
 
10.3%

Most occurring characters

ValueCountFrequency (%)
i18378
21.7%
n12243
14.5%
E7510
8.9%
g7510
8.9%
l7510
8.9%
s7510
8.9%
h7510
8.9%
H4733
 
5.6%
d4733
 
5.6%
N1402
 
1.7%
Other values (4)5608
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter71002
83.9%
Uppercase Letter13645
 
16.1%

Most frequent character per category

ValueCountFrequency (%)
i18378
25.9%
n12243
17.2%
g7510
10.6%
l7510
10.6%
s7510
10.6%
h7510
10.6%
d4733
 
6.7%
a1402
 
2.0%
t1402
 
2.0%
v1402
 
2.0%
ValueCountFrequency (%)
E7510
55.0%
H4733
34.7%
N1402
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin84647
100.0%

Most frequent character per script

ValueCountFrequency (%)
i18378
21.7%
n12243
14.5%
E7510
8.9%
g7510
8.9%
l7510
8.9%
s7510
8.9%
h7510
8.9%
H4733
 
5.6%
d4733
 
5.6%
N1402
 
1.7%
Other values (4)5608
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII84647
100.0%

Most frequent character per block

ValueCountFrequency (%)
i18378
21.7%
n12243
14.5%
E7510
8.9%
g7510
8.9%
l7510
8.9%
s7510
8.9%
h7510
8.9%
H4733
 
5.6%
d4733
 
5.6%
N1402
 
1.7%
Other values (4)5608
 
6.6%

Age
Real number (ℝ≥0)

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.69124221
Minimum24
Maximum42
Zeros0
Zeros (%)0.0%
Memory size213.2 KiB
2021-06-06T20:09:18.584329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile24
Q128
median31
Q334
95-th percentile37
Maximum42
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.881378423
Coefficient of variation (CV)0.1264653414
Kurtosis-0.6219239727
Mean30.69124221
Median Absolute Deviation (MAD)3
Skewness0.1622960054
Sum418782
Variance15.06509846
MonotocityNot monotonic
2021-06-06T20:09:18.896567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
321207
8.8%
341157
8.5%
291140
8.4%
331140
8.4%
351122
8.2%
311112
8.1%
281105
8.1%
271100
 
8.1%
301060
 
7.8%
26744
 
5.5%
Other values (9)2758
20.2%
ValueCountFrequency (%)
24721
5.3%
25733
5.4%
26744
5.5%
271100
8.1%
281105
8.1%
291140
8.4%
301060
7.8%
311112
8.1%
321207
8.8%
331140
8.4%
ValueCountFrequency (%)
4241
 
0.3%
4162
 
0.5%
4056
 
0.4%
3952
 
0.4%
38365
 
2.7%
37375
 
2.7%
36353
 
2.6%
351122
8.2%
341157
8.5%
331140
8.4%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size928.8 KiB
Male
8376 
Female
4326 
Other
943 

Length

Max length6
Median length4
Mean length4.703187981
Min length4

Characters and Unicode

Total characters64175
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowMale
ValueCountFrequency (%)
Male8376
61.4%
Female4326
31.7%
Other943
 
6.9%
2021-06-06T20:09:19.615442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:19.891029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male8376
61.4%
female4326
31.7%
other943
 
6.9%

Most occurring characters

ValueCountFrequency (%)
e17971
28.0%
a12702
19.8%
l12702
19.8%
M8376
13.1%
F4326
 
6.7%
m4326
 
6.7%
O943
 
1.5%
t943
 
1.5%
h943
 
1.5%
r943
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50530
78.7%
Uppercase Letter13645
 
21.3%

Most frequent character per category

ValueCountFrequency (%)
e17971
35.6%
a12702
25.1%
l12702
25.1%
m4326
 
8.6%
t943
 
1.9%
h943
 
1.9%
r943
 
1.9%
ValueCountFrequency (%)
M8376
61.4%
F4326
31.7%
O943
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin64175
100.0%

Most frequent character per script

ValueCountFrequency (%)
e17971
28.0%
a12702
19.8%
l12702
19.8%
M8376
13.1%
F4326
 
6.7%
m4326
 
6.7%
O943
 
1.5%
t943
 
1.5%
h943
 
1.5%
r943
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII64175
100.0%

Most frequent character per block

ValueCountFrequency (%)
e17971
28.0%
a12702
19.8%
l12702
19.8%
M8376
13.1%
F4326
 
6.7%
m4326
 
6.7%
O943
 
1.5%
t943
 
1.5%
h943
 
1.5%
r943
 
1.5%
Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size959.4 KiB
JR85289
4697 
JR87525
3416 
JR88879
1231 
JR89890
1157 
JR88873
1123 
Other values (4)
2021 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters95515
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJR85289
2nd rowJR87525
3rd rowJR87525
4th rowJR87525
5th rowJR70175
ValueCountFrequency (%)
JR852894697
34.4%
JR875253416
25.0%
JR888791231
 
9.0%
JR898901157
 
8.5%
JR888731123
 
8.2%
JR81165681
 
5.0%
JR79193678
 
5.0%
JR88654420
 
3.1%
JR70175242
 
1.8%
2021-06-06T20:09:20.493296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:20.714847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
jr852894697
34.4%
jr875253416
25.0%
jr888791231
 
9.0%
jr898901157
 
8.5%
jr888731123
 
8.2%
jr81165681
 
5.0%
jr79193678
 
5.0%
jr88654420
 
3.1%
jr70175242
 
1.8%

Most occurring characters

ValueCountFrequency (%)
823707
24.8%
J13645
14.3%
R13645
14.3%
512872
13.5%
99598
10.0%
28113
 
8.5%
76932
 
7.3%
12282
 
2.4%
31801
 
1.9%
01399
 
1.5%
Other values (2)1521
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number68225
71.4%
Uppercase Letter27290
 
28.6%

Most frequent character per category

ValueCountFrequency (%)
823707
34.7%
512872
18.9%
99598
14.1%
28113
 
11.9%
76932
 
10.2%
12282
 
3.3%
31801
 
2.6%
01399
 
2.1%
61101
 
1.6%
4420
 
0.6%
ValueCountFrequency (%)
J13645
50.0%
R13645
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common68225
71.4%
Latin27290
 
28.6%

Most frequent character per script

ValueCountFrequency (%)
823707
34.7%
512872
18.9%
99598
14.1%
28113
 
11.9%
76932
 
10.2%
12282
 
3.3%
31801
 
2.6%
01399
 
2.1%
61101
 
1.6%
4420
 
0.6%
ValueCountFrequency (%)
J13645
50.0%
R13645
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII95515
100.0%

Most frequent character per block

ValueCountFrequency (%)
823707
24.8%
J13645
14.3%
R13645
14.3%
512872
13.5%
99598
10.0%
28113
 
8.5%
76932
 
7.3%
12282
 
2.4%
31801
 
1.9%
01399
 
1.5%
Other values (2)1521
 
1.6%

HighestDegree
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size940.8 KiB
B.Tech
5619 
M.Tech
2026 
BCA
1634 
MS
1579 
Dual M.Tech
1358 
Other values (3)
1429 

Length

Max length11
Median length6
Mean length5.60498351
Min length2

Characters and Unicode

Total characters76480
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB.Tech
2nd rowB.Tech
3rd rowPhD
4th rowBCA
5th rowDual M.Tech
ValueCountFrequency (%)
B.Tech5619
41.2%
M.Tech2026
 
14.8%
BCA1634
 
12.0%
MS1579
 
11.6%
Dual M.Tech1358
 
10.0%
Dual MBA665
 
4.9%
PhD624
 
4.6%
MCA140
 
1.0%
2021-06-06T20:09:21.519271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:21.743691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
b.tech5619
35.9%
m.tech3384
21.6%
dual2023
 
12.9%
bca1634
 
10.4%
ms1579
 
10.1%
mba665
 
4.2%
phd624
 
4.0%
mca140
 
0.9%

Most occurring characters

ValueCountFrequency (%)
h9627
12.6%
.9003
11.8%
T9003
11.8%
e9003
11.8%
c9003
11.8%
B7918
10.4%
M5768
7.5%
D2647
 
3.5%
A2439
 
3.2%
u2023
 
2.6%
Other values (6)10046
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter33702
44.1%
Uppercase Letter31752
41.5%
Other Punctuation9003
 
11.8%
Space Separator2023
 
2.6%

Most frequent character per category

ValueCountFrequency (%)
T9003
28.4%
B7918
24.9%
M5768
18.2%
D2647
 
8.3%
A2439
 
7.7%
C1774
 
5.6%
S1579
 
5.0%
P624
 
2.0%
ValueCountFrequency (%)
h9627
28.6%
e9003
26.7%
c9003
26.7%
u2023
 
6.0%
a2023
 
6.0%
l2023
 
6.0%
ValueCountFrequency (%)
.9003
100.0%
ValueCountFrequency (%)
2023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin65454
85.6%
Common11026
 
14.4%

Most frequent character per script

ValueCountFrequency (%)
h9627
14.7%
T9003
13.8%
e9003
13.8%
c9003
13.8%
B7918
12.1%
M5768
8.8%
D2647
 
4.0%
A2439
 
3.7%
u2023
 
3.1%
a2023
 
3.1%
Other values (4)6000
9.2%
ValueCountFrequency (%)
.9003
81.7%
2023
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII76480
100.0%

Most frequent character per block

ValueCountFrequency (%)
h9627
12.6%
.9003
11.8%
T9003
11.8%
e9003
11.8%
c9003
11.8%
B7918
10.4%
M5768
7.5%
D2647
 
3.5%
A2439
 
3.2%
u2023
 
2.6%
Other values (6)10046
13.1%

DegreeBranch
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Computer Science
4749 
Information Technology
2993 
Artificial Intelligence
2001 
Electrical
1803 
Electronics
1699 

Length

Max length26
Median length16
Mean length17.22037376
Min length10

Characters and Unicode

Total characters234972
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectrical
2nd rowArtificial Intelligence
3rd rowComputer Science
4th rowInformation Technology
5th rowComputer Science
ValueCountFrequency (%)
Computer Science4749
34.8%
Information Technology2993
21.9%
Artificial Intelligence2001
14.7%
Electrical1803
 
13.2%
Electronics1699
 
12.5%
Electrical and Electronics400
 
2.9%
2021-06-06T20:09:22.538090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:22.807355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
science4749
19.6%
computer4749
19.6%
technology2993
12.4%
information2993
12.4%
electrical2203
9.1%
electronics2099
8.7%
artificial2001
8.3%
intelligence2001
8.3%
and400
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e27545
11.7%
c25097
10.7%
n20229
 
8.6%
i20048
 
8.5%
o18820
 
8.0%
t16046
 
6.8%
l15501
 
6.6%
r14045
 
6.0%
10543
 
4.5%
m7742
 
3.3%
Other values (15)59356
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter200641
85.4%
Uppercase Letter23788
 
10.1%
Space Separator10543
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
e27545
13.7%
c25097
12.5%
n20229
10.1%
i20048
10.0%
o18820
9.4%
t16046
8.0%
l15501
7.7%
r14045
7.0%
m7742
 
3.9%
a7597
 
3.8%
Other values (8)27971
13.9%
ValueCountFrequency (%)
I4994
21.0%
C4749
20.0%
S4749
20.0%
E4302
18.1%
T2993
12.6%
A2001
8.4%
ValueCountFrequency (%)
10543
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin224429
95.5%
Common10543
 
4.5%

Most frequent character per script

ValueCountFrequency (%)
e27545
12.3%
c25097
11.2%
n20229
 
9.0%
i20048
 
8.9%
o18820
 
8.4%
t16046
 
7.1%
l15501
 
6.9%
r14045
 
6.3%
m7742
 
3.4%
a7597
 
3.4%
Other values (14)51759
23.1%
ValueCountFrequency (%)
10543
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII234972
100.0%

Most frequent character per block

ValueCountFrequency (%)
e27545
11.7%
c25097
10.7%
n20229
 
8.6%
i20048
 
8.5%
o18820
 
8.0%
t16046
 
6.8%
l15501
 
6.6%
r14045
 
6.0%
10543
 
4.5%
m7742
 
3.3%
Other values (15)59356
25.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size946.1 KiB
Tier 2
6092 
Tier 1
4793 
Tier 3
2760 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters81870
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTier 1
2nd rowTier 3
3rd rowTier 1
4th rowTier 2
5th rowTier 3
ValueCountFrequency (%)
Tier 26092
44.6%
Tier 14793
35.1%
Tier 32760
20.2%
2021-06-06T20:09:23.515316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:23.740546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
tier13645
50.0%
26092
22.3%
14793
 
17.6%
32760
 
10.1%

Most occurring characters

ValueCountFrequency (%)
T13645
16.7%
i13645
16.7%
e13645
16.7%
r13645
16.7%
13645
16.7%
26092
7.4%
14793
 
5.9%
32760
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40935
50.0%
Uppercase Letter13645
 
16.7%
Space Separator13645
 
16.7%
Decimal Number13645
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
i13645
33.3%
e13645
33.3%
r13645
33.3%
ValueCountFrequency (%)
26092
44.6%
14793
35.1%
32760
20.2%
ValueCountFrequency (%)
T13645
100.0%
ValueCountFrequency (%)
13645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54580
66.7%
Common27290
33.3%

Most frequent character per script

ValueCountFrequency (%)
T13645
25.0%
i13645
25.0%
e13645
25.0%
r13645
25.0%
ValueCountFrequency (%)
13645
50.0%
26092
22.3%
14793
 
17.6%
32760
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII81870
100.0%

Most frequent character per block

ValueCountFrequency (%)
T13645
16.7%
i13645
16.7%
e13645
16.7%
r13645
16.7%
13645
16.7%
26092
7.4%
14793
 
5.9%
32760
 
3.4%

LatestDegreeCGPA
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.100256504
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Memory size213.2 KiB
2021-06-06T20:09:23.969481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16
median7
Q38
95-th percentile9
Maximum10
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.346538857
Coefficient of variation (CV)0.1896465087
Kurtosis-0.0380233269
Mean7.100256504
Median Absolute Deviation (MAD)1
Skewness-0.4023318346
Sum96883
Variance1.813166894
MonotocityNot monotonic
2021-06-06T20:09:24.200994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
74738
34.7%
83480
25.5%
61779
 
13.0%
91554
 
11.4%
51089
 
8.0%
4713
 
5.2%
10292
 
2.1%
ValueCountFrequency (%)
4713
 
5.2%
51089
 
8.0%
61779
 
13.0%
74738
34.7%
83480
25.5%
91554
 
11.4%
10292
 
2.1%
ValueCountFrequency (%)
10292
 
2.1%
91554
 
11.4%
83480
25.5%
74738
34.7%
61779
 
13.0%
51089
 
8.0%
4713
 
5.2%

YearsOfExperince
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.547746427
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size213.2 KiB
2021-06-06T20:09:24.467720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.426919995
Coefficient of variation (CV)0.5233739628
Kurtosis-1.202600586
Mean6.547746427
Median Absolute Deviation (MAD)3
Skewness-0.0155005509
Sum89344
Variance11.74378065
MonotocityNot monotonic
2021-06-06T20:09:24.738535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
91219
8.9%
61196
8.8%
111166
8.5%
41146
8.4%
51142
8.4%
71130
8.3%
121123
8.2%
101116
8.2%
81114
8.2%
31109
8.1%
Other values (2)2184
16.0%
ValueCountFrequency (%)
11076
7.9%
21108
8.1%
31109
8.1%
41146
8.4%
51142
8.4%
61196
8.8%
71130
8.3%
81114
8.2%
91219
8.9%
101116
8.2%
ValueCountFrequency (%)
121123
8.2%
111166
8.5%
101116
8.2%
91219
8.9%
81114
8.2%
71130
8.3%
61196
8.8%
51142
8.4%
41146
8.4%
31109
8.1%

GraduationYear
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.452254
Minimum2009
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size213.2 KiB
2021-06-06T20:09:25.032953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2014
Q32017
95-th percentile2020
Maximum2020
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.426919995
Coefficient of variation (CV)0.001701167148
Kurtosis-1.202600586
Mean2014.452254
Median Absolute Deviation (MAD)3
Skewness0.0155005509
Sum27487201
Variance11.74378065
MonotocityNot monotonic
2021-06-06T20:09:25.313033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20121219
8.9%
20151196
8.8%
20101166
8.5%
20171146
8.4%
20161142
8.4%
20141130
8.3%
20091123
8.2%
20111116
8.2%
20131114
8.2%
20181109
8.1%
Other values (2)2184
16.0%
ValueCountFrequency (%)
20091123
8.2%
20101166
8.5%
20111116
8.2%
20121219
8.9%
20131114
8.2%
20141130
8.3%
20151196
8.8%
20161142
8.4%
20171146
8.4%
20181109
8.1%
ValueCountFrequency (%)
20201076
7.9%
20191108
8.1%
20181109
8.1%
20171146
8.4%
20161142
8.4%
20151196
8.8%
20141130
8.3%
20131114
8.2%
20121219
8.9%
20111116
8.2%

CurrentCTC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.07695126
Minimum6
Maximum30
Zeros0
Zeros (%)0.0%
Memory size213.2 KiB
2021-06-06T20:09:25.616451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q112
median18
Q324
95-th percentile29
Maximum30
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.208130046
Coefficient of variation (CV)0.3987469978
Kurtosis-1.201778344
Mean18.07695126
Median Absolute Deviation (MAD)6
Skewness-0.01016598468
Sum246660
Variance51.95713875
MonotocityNot monotonic
2021-06-06T20:09:26.169051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
19598
 
4.4%
28589
 
4.3%
16575
 
4.2%
25574
 
4.2%
11572
 
4.2%
24567
 
4.2%
17562
 
4.1%
10559
 
4.1%
23556
 
4.1%
29556
 
4.1%
Other values (15)7937
58.2%
ValueCountFrequency (%)
6541
4.0%
7537
3.9%
8511
3.7%
9526
3.9%
10559
4.1%
11572
4.2%
12543
4.0%
13530
3.9%
14518
3.8%
15532
3.9%
ValueCountFrequency (%)
30554
4.1%
29556
4.1%
28589
4.3%
27517
3.8%
26526
3.9%
25574
4.2%
24567
4.2%
23556
4.1%
22526
3.9%
21544
4.0%

ExpectedCTC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.06148772
Minimum10
Maximum40
Zeros0
Zeros (%)0.0%
Memory size213.2 KiB
2021-06-06T20:09:26.498414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q119
median25
Q331
95-th percentile37
Maximum40
Range30
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.47811922
Coefficient of variation (CV)0.2983908738
Kurtosis-1.029886953
Mean25.06148772
Median Absolute Deviation (MAD)6
Skewness0.0004423838647
Sum341964
Variance55.92226706
MonotocityNot monotonic
2021-06-06T20:09:26.816044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
31600
 
4.4%
27598
 
4.4%
21584
 
4.3%
25569
 
4.2%
20559
 
4.1%
16555
 
4.1%
33551
 
4.0%
28544
 
4.0%
23544
 
4.0%
29538
 
3.9%
Other values (21)8003
58.7%
ValueCountFrequency (%)
1083
 
0.6%
11142
 
1.0%
12231
1.7%
13283
2.1%
14418
3.1%
15454
3.3%
16555
4.1%
17515
3.8%
18530
3.9%
19537
3.9%
ValueCountFrequency (%)
4081
 
0.6%
39171
 
1.3%
38264
1.9%
37317
2.3%
36366
2.7%
35463
3.4%
34531
3.9%
33551
4.0%
32501
3.7%
31600
4.4%

MartialStatus
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size955.7 KiB
Married
9810 
Single
3835 

Length

Max length7
Median length7
Mean length6.718944668
Min length6

Characters and Unicode

Total characters91680
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried
ValueCountFrequency (%)
Married9810
71.9%
Single3835
 
28.1%
2021-06-06T20:09:27.470747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:27.665887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
married9810
71.9%
single3835
 
28.1%

Most occurring characters

ValueCountFrequency (%)
r19620
21.4%
i13645
14.9%
e13645
14.9%
M9810
10.7%
a9810
10.7%
d9810
10.7%
S3835
 
4.2%
n3835
 
4.2%
g3835
 
4.2%
l3835
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter78035
85.1%
Uppercase Letter13645
 
14.9%

Most frequent character per category

ValueCountFrequency (%)
r19620
25.1%
i13645
17.5%
e13645
17.5%
a9810
12.6%
d9810
12.6%
n3835
 
4.9%
g3835
 
4.9%
l3835
 
4.9%
ValueCountFrequency (%)
M9810
71.9%
S3835
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Latin91680
100.0%

Most frequent character per script

ValueCountFrequency (%)
r19620
21.4%
i13645
14.9%
e13645
14.9%
M9810
10.7%
a9810
10.7%
d9810
10.7%
S3835
 
4.2%
n3835
 
4.2%
g3835
 
4.2%
l3835
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII91680
100.0%

Most frequent character per block

ValueCountFrequency (%)
r19620
21.4%
i13645
14.9%
e13645
14.9%
M9810
10.7%
a9810
10.7%
d9810
10.7%
S3835
 
4.2%
n3835
 
4.2%
g3835
 
4.2%
l3835
 
4.2%

EmpScore
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size879.5 KiB
3
5462 
4
4184 
5
2025 
2
1064 
1
910 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13645
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5
ValueCountFrequency (%)
35462
40.0%
44184
30.7%
52025
 
14.8%
21064
 
7.8%
1910
 
6.7%
2021-06-06T20:09:28.242372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:28.461130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
35462
40.0%
44184
30.7%
52025
 
14.8%
21064
 
7.8%
1910
 
6.7%

Most occurring characters

ValueCountFrequency (%)
35462
40.0%
44184
30.7%
52025
 
14.8%
21064
 
7.8%
1910
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number13645
100.0%

Most frequent character per category

ValueCountFrequency (%)
35462
40.0%
44184
30.7%
52025
 
14.8%
21064
 
7.8%
1910
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common13645
100.0%

Most frequent character per script

ValueCountFrequency (%)
35462
40.0%
44184
30.7%
52025
 
14.8%
21064
 
7.8%
1910
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII13645
100.0%

Most frequent character per block

ValueCountFrequency (%)
35462
40.0%
44184
30.7%
52025
 
14.8%
21064
 
7.8%
1910
 
6.7%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size901.0 KiB
SDE
5423 
SSE
2745 
DS
1716 
BA
1710 
EM
1342 
Other values (3)
709 

Length

Max length3
Median length3
Mean length2.619640894
Min length2

Characters and Unicode

Total characters35745
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSE
2nd rowBA
3rd rowSDE
4th rowSDE
5th rowSDE
ValueCountFrequency (%)
SDE5423
39.7%
SSE2745
20.1%
DS1716
 
12.6%
BA1710
 
12.5%
EM1342
 
9.8%
DA287
 
2.1%
SEM287
 
2.1%
DE135
 
1.0%
2021-06-06T20:09:29.124966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:29.366885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sde5423
39.7%
sse2745
20.1%
ds1716
 
12.6%
ba1710
 
12.5%
em1342
 
9.8%
sem287
 
2.1%
da287
 
2.1%
de135
 
1.0%

Most occurring characters

ValueCountFrequency (%)
S12916
36.1%
E9932
27.8%
D7561
21.2%
A1997
 
5.6%
B1710
 
4.8%
M1629
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter35745
100.0%

Most frequent character per category

ValueCountFrequency (%)
S12916
36.1%
E9932
27.8%
D7561
21.2%
A1997
 
5.6%
B1710
 
4.8%
M1629
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Latin35745
100.0%

Most frequent character per script

ValueCountFrequency (%)
S12916
36.1%
E9932
27.8%
D7561
21.2%
A1997
 
5.6%
B1710
 
4.8%
M1629
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII35745
100.0%

Most frequent character per block

ValueCountFrequency (%)
S12916
36.1%
E9932
27.8%
D7561
21.2%
A1997
 
5.6%
B1710
 
4.8%
M1629
 
4.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size975.1 KiB
Startup
6373 
Enterprise
4395 
MidSized
2877 

Length

Max length10
Median length8
Mean length8.177134481
Min length7

Characters and Unicode

Total characters111577
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnterprise
2nd rowMidSized
3rd rowMidSized
4th rowStartup
5th rowEnterprise
ValueCountFrequency (%)
Startup6373
46.7%
Enterprise4395
32.2%
MidSized2877
21.1%
2021-06-06T20:09:30.123466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:30.338819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
startup6373
46.7%
enterprise4395
32.2%
midsized2877
21.1%

Most occurring characters

ValueCountFrequency (%)
t17141
15.4%
r15163
13.6%
e11667
10.5%
p10768
9.7%
i10149
9.1%
S9250
8.3%
a6373
 
5.7%
u6373
 
5.7%
d5754
 
5.2%
E4395
 
3.9%
Other values (4)14544
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter95055
85.2%
Uppercase Letter16522
 
14.8%

Most frequent character per category

ValueCountFrequency (%)
t17141
18.0%
r15163
16.0%
e11667
12.3%
p10768
11.3%
i10149
10.7%
a6373
 
6.7%
u6373
 
6.7%
d5754
 
6.1%
n4395
 
4.6%
s4395
 
4.6%
ValueCountFrequency (%)
S9250
56.0%
E4395
26.6%
M2877
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Latin111577
100.0%

Most frequent character per script

ValueCountFrequency (%)
t17141
15.4%
r15163
13.6%
e11667
10.5%
p10768
9.7%
i10149
9.1%
S9250
8.3%
a6373
 
5.7%
u6373
 
5.7%
d5754
 
5.2%
E4395
 
3.9%
Other values (4)14544
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII111577
100.0%

Most frequent character per block

ValueCountFrequency (%)
t17141
15.4%
r15163
13.6%
e11667
10.5%
p10768
9.7%
i10149
9.1%
S9250
8.3%
a6373
 
5.7%
u6373
 
5.7%
d5754
 
5.2%
E4395
 
3.9%
Other values (4)14544
13.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size991.4 KiB
Engineering
5749 
Design
4393 
Customer Success
1570 
Product
1232 
Finance
701 

Length

Max length16
Median length11
Mean length9.398900696
Min length6

Characters and Unicode

Total characters128248
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesign
2nd rowEngineering
3rd rowEngineering
4th rowProduct
5th rowEngineering
ValueCountFrequency (%)
Engineering5749
42.1%
Design4393
32.2%
Customer Success1570
 
11.5%
Product1232
 
9.0%
Finance701
 
5.1%
2021-06-06T20:09:30.929035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:31.139947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
engineering5749
37.8%
design4393
28.9%
customer1570
 
10.3%
success1570
 
10.3%
product1232
 
8.1%
finance701
 
4.6%

Most occurring characters

ValueCountFrequency (%)
n23042
18.0%
e19732
15.4%
i16592
12.9%
g15891
12.4%
s9103
 
7.1%
r8551
 
6.7%
E5749
 
4.5%
c5073
 
4.0%
D4393
 
3.4%
u4372
 
3.4%
Other values (10)15750
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter111463
86.9%
Uppercase Letter15215
 
11.9%
Space Separator1570
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
n23042
20.7%
e19732
17.7%
i16592
14.9%
g15891
14.3%
s9103
 
8.2%
r8551
 
7.7%
c5073
 
4.6%
u4372
 
3.9%
o2802
 
2.5%
t2802
 
2.5%
Other values (3)3503
 
3.1%
ValueCountFrequency (%)
E5749
37.8%
D4393
28.9%
C1570
 
10.3%
S1570
 
10.3%
P1232
 
8.1%
F701
 
4.6%
ValueCountFrequency (%)
1570
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin126678
98.8%
Common1570
 
1.2%

Most frequent character per script

ValueCountFrequency (%)
n23042
18.2%
e19732
15.6%
i16592
13.1%
g15891
12.5%
s9103
 
7.2%
r8551
 
6.8%
E5749
 
4.5%
c5073
 
4.0%
D4393
 
3.5%
u4372
 
3.5%
Other values (9)14180
11.2%
ValueCountFrequency (%)
1570
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII128248
100.0%

Most frequent character per block

ValueCountFrequency (%)
n23042
18.0%
e19732
15.4%
i16592
12.9%
g15891
12.4%
s9103
 
7.1%
r8551
 
6.7%
E5749
 
4.5%
c5073
 
4.0%
D4393
 
3.4%
u4372
 
3.4%
Other values (10)15750
12.3%

TotalLeavesTaken
Real number (ℝ≥0)

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.96702089
Minimum4
Maximum20
Zeros0
Zeros (%)0.0%
Memory size213.2 KiB
2021-06-06T20:09:31.514289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q18
median12
Q316
95-th percentile20
Maximum20
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.897836049
Coefficient of variation (CV)0.4092778057
Kurtosis-1.195142977
Mean11.96702089
Median Absolute Deviation (MAD)4
Skewness0.01027985964
Sum163290
Variance23.98879796
MonotocityNot monotonic
2021-06-06T20:09:31.790899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11861
 
6.3%
6856
 
6.3%
13842
 
6.2%
4827
 
6.1%
12818
 
6.0%
20812
 
6.0%
14802
 
5.9%
19802
 
5.9%
10794
 
5.8%
18792
 
5.8%
Other values (7)5439
39.9%
ValueCountFrequency (%)
4827
6.1%
5787
5.8%
6856
6.3%
7752
5.5%
8792
5.8%
9792
5.8%
10794
5.8%
11861
6.3%
12818
6.0%
13842
6.2%
ValueCountFrequency (%)
20812
6.0%
19802
5.9%
18792
5.8%
17773
5.7%
16762
5.6%
15781
5.7%
14802
5.9%
13842
6.2%
12818
6.0%
11861
6.3%

BiasInfluentialFactor
Categorical

MISSING

Distinct9
Distinct (%)0.1%
Missing3336
Missing (%)24.4%
Memory size901.9 KiB
DegreeBranch
2858 
Gender
2605 
YearsOfExperince
1561 
CurrentCompanyType
1174 
EmpScore
659 
Other values (4)
1452 

Length

Max length18
Median length12
Mean length11.63876225
Min length6

Characters and Unicode

Total characters119984
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYearsOfExperince
2nd rowGender
3rd rowGender
4th rowDegreeBranch
5th rowYearsOfExperince
ValueCountFrequency (%)
DegreeBranch2858
20.9%
Gender2605
19.1%
YearsOfExperince1561
11.4%
CurrentCompanyType1174
 
8.6%
EmpScore659
 
4.8%
HighestDegree582
 
4.3%
Ethinicity328
 
2.4%
MartialStatus280
 
2.1%
LatestDegreeCGPA262
 
1.9%
(Missing)3336
24.4%
2021-06-06T20:09:32.755221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-06-06T20:09:33.208780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
degreebranch2858
27.7%
gender2605
25.3%
yearsofexperince1561
15.1%
currentcompanytype1174
11.4%
empscore659
 
6.4%
highestdegree582
 
5.6%
ethinicity328
 
3.2%
martialstatus280
 
2.7%
latestdegreecgpa262
 
2.5%

Most occurring characters

ValueCountFrequency (%)
e24850
20.7%
r15574
13.0%
n9700
 
8.1%
a6695
 
5.6%
c5406
 
4.5%
p4568
 
3.8%
g4284
 
3.6%
t3776
 
3.1%
h3768
 
3.1%
D3702
 
3.1%
Other values (23)37661
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter98516
82.1%
Uppercase Letter21468
 
17.9%

Most frequent character per category

ValueCountFrequency (%)
e24850
25.2%
r15574
15.8%
n9700
 
9.8%
a6695
 
6.8%
c5406
 
5.5%
p4568
 
4.6%
g4284
 
4.3%
t3776
 
3.8%
h3768
 
3.8%
i3407
 
3.5%
Other values (9)16488
16.7%
ValueCountFrequency (%)
D3702
17.2%
G2867
13.4%
B2858
13.3%
C2610
12.2%
E2548
11.9%
Y1561
7.3%
O1561
7.3%
T1174
 
5.5%
S939
 
4.4%
H582
 
2.7%
Other values (4)1066
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119984
100.0%

Most frequent character per script

ValueCountFrequency (%)
e24850
20.7%
r15574
13.0%
n9700
 
8.1%
a6695
 
5.6%
c5406
 
4.5%
p4568
 
3.8%
g4284
 
3.6%
t3776
 
3.1%
h3768
 
3.1%
D3702
 
3.1%
Other values (23)37661
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII119984
100.0%

Most frequent character per block

ValueCountFrequency (%)
e24850
20.7%
r15574
13.0%
n9700
 
8.1%
a6695
 
5.6%
c5406
 
4.5%
p4568
 
3.8%
g4284
 
3.6%
t3776
 
3.1%
h3768
 
3.1%
D3702
 
3.1%
Other values (23)37661
31.4%

FitmentPercent
Real number (ℝ≥0)

Distinct4578
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.88009307
Minimum33.53
Maximum95.5
Zeros0
Zeros (%)0.0%
Memory size213.2 KiB
2021-06-06T20:09:33.792943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum33.53
5-th percentile42.764
Q168.72
median78.11
Q387.99
95-th percentile93.69
Maximum95.5
Range61.97
Interquartile range (IQR)19.27

Descriptive statistics

Standard deviation14.89420711
Coefficient of variation (CV)0.1962860944
Kurtosis0.3609468789
Mean75.88009307
Median Absolute Deviation (MAD)9.64
Skewness-0.9661750482
Sum1035383.87
Variance221.8374055
MonotocityNot monotonic
2021-06-06T20:09:34.280441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.213
 
0.1%
91.5913
 
0.1%
91.0412
 
0.1%
71.812
 
0.1%
85.7512
 
0.1%
89.2211
 
0.1%
86.7411
 
0.1%
89.1811
 
0.1%
74.3411
 
0.1%
88.8511
 
0.1%
Other values (4568)13528
99.1%
ValueCountFrequency (%)
33.532
< 0.1%
33.582
< 0.1%
33.61
 
< 0.1%
33.622
< 0.1%
33.651
 
< 0.1%
33.661
 
< 0.1%
33.671
 
< 0.1%
33.711
 
< 0.1%
33.753
< 0.1%
33.761
 
< 0.1%
ValueCountFrequency (%)
95.54
< 0.1%
95.497
0.1%
95.482
 
< 0.1%
95.472
 
< 0.1%
95.462
 
< 0.1%
95.455
< 0.1%
95.443
< 0.1%
95.431
 
< 0.1%
95.423
< 0.1%
95.411
 
< 0.1%

Interactions

2021-06-06T20:08:52.659610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:53.055296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:53.516741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:53.880088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:54.320721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:54.704029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:55.159100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:55.620004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:56.006030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:56.446330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:57.011255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:57.456260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:57.920594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:58.291669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:58.629057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:59.006748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:59.370253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:08:59.813263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:00.193061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:00.694781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:01.110067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:01.483139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:02.060208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:02.422568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:02.815692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:03.156332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:03.514469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:03.899275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:04.244319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:04.604556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:04.951235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:05.277820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:05.613331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:05.969530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:06.339148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:06.668613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:07.077840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:07.434166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:07.851181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:08.207384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:08.544214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:08.914805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:09.261917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:09.645099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:10.031844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:10.427852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:10.816742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:11.185194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:11.557897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:11.932811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:12.282465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:12.615362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:12.981318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:13.318674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:13.820778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-06T20:09:14.156460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-06-06T20:09:34.846002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-06T20:09:35.622162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-06T20:09:36.184959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-06T20:09:36.798881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-06T20:09:37.954933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-06T20:09:14.907919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-06T20:09:16.290307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-06T20:09:16.919599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

LanguageOfCommunicationAgeGenderJobProfileIDApplyingForHighestDegreeDegreeBranchGraduatingInstituteLatestDegreeCGPAYearsOfExperinceGraduationYearCurrentCTCExpectedCTCMartialStatusEmpScoreCurrentDesignationCurrentCompanyTypeDepartmentInCompanyTotalLeavesTakenBiasInfluentialFactorFitmentPercent
0English35MaleJR85289B.TechElectricalTier 171220092126Married5SSEEnterpriseDesign20YearsOfExperince95.40
1English26MaleJR87525B.TechArtificial IntelligenceTier 37320181519Married5BAMidSizedEngineering6NaN67.09
2English36FemaleJR87525PhDComputer ScienceTier 16620151524Single5SDEMidSizedEngineering19Gender91.26
3English29FemaleJR87525BCAInformation TechnologyTier 25620151624Married5SDEStartupProduct16Gender72.29
4English25MaleJR70175Dual M.TechComputer ScienceTier 38220192432Married5SDEEnterpriseEngineering10DegreeBranch86.34
5Native35MaleJR88879BCAComputer ScienceTier 291220092529Married4DSMidSizedEngineering10YearsOfExperince93.23
6Hindi31MaleJR85289PhDComputer ScienceTier 17120201221Single3SDEEnterpriseCustomer Success8CurrentCompanyType62.29
7English32MaleJR85289B.TechInformation TechnologyTier 2892012717Married3SSEMidSizedEngineering18DegreeBranch93.71
8English28FemaleJR87525M.TechElectricalTier 16220192128Married4SDEStartupEngineering7Gender91.66
9Native31FemaleJR88873B.TechArtificial IntelligenceTier 28820132131Married3SDEStartupCustomer Success10Gender73.31

Last rows

LanguageOfCommunicationAgeGenderJobProfileIDApplyingForHighestDegreeDegreeBranchGraduatingInstituteLatestDegreeCGPAYearsOfExperinceGraduationYearCurrentCTCExpectedCTCMartialStatusEmpScoreCurrentDesignationCurrentCompanyTypeDepartmentInCompanyTotalLeavesTakenBiasInfluentialFactorFitmentPercent
13635English35MaleJR85289PhDArtificial IntelligenceTier 25520161119Married5SDEMidSizedProduct10NaN69.49
13636English29MaleJR85289MCAArtificial IntelligenceTier 210320182737Married3SEMEnterpriseEngineering9LatestDegreeCGPA57.14
13637Hindi29MaleJR85289BCAElectronicsTier 1662015919Married3SDEStartupProduct18MartialStatus82.27
13638English37MaleJR87525M.TechInformation TechnologyTier 28112010818Single3SSEMidSizedDesign11DegreeBranch88.15
13639Hindi31FemaleJR88873BCAComputer ScienceTier 1482013616Married3EMStartupEngineering14Gender91.19
13640English25FemaleJR87525Dual MBAElectricalTier 27220193034Married3SDEStartupEngineering5Gender93.65
13641Native29OtherJR87525Dual M.TechComputer ScienceTier 17620152228Married4SSEStartupCustomer Success14NaN52.90
13642Hindi37MaleJR88873M.TechArtificial IntelligenceTier 18112010816Single4SDEEnterpriseDesign20CurrentCompanyType61.46
13643Hindi24MaleJR87525B.TechElectricalTier 24120202736Single4SDEMidSizedEngineering7EmpScore93.64
13644English35MaleJR87525B.TechElectricalTier 271220091724Single3SSEStartupEngineering16YearsOfExperince93.52